Title :
Comparison of remote sensing based forest area and change estimation with national forestry inventory between 2000 and 2005 in China
Author :
Dan-Xia Song ; Chengquan Huang ; Noojipady, Praveen ; Channan, Saurabh ; Townshend, John
Author_Institution :
Dept. of Geogr. Sci., Univ. of Maryland, College Park, MD, USA
Abstract :
Moderate resolution remote sensing data, such as Landsat satellite image, has been widely used in mapping land cover land use and changes at regional scale. However, at national to global scale, remote sensing data were more often used at designed samples or for interpretation of remote regions, which has resulted in gaps between remote sensing and inventory derived estimation. We used the GLCF forest cover change (GFCC) map at a 30-meter resolution between 2000 and 2005 produced by the Global Land Cover Facility (GLCF), and compared the GFCC estimation against the report from China´s National Forest Inventory (NFI). Two forest definitions, minimum 20% and 30% tree cover, were used in mapping to match forest definitions used by the International Geosphere-Biosphere Programme (IGBP) and NFI respectively. Significant disagreements were observed: for provinces with low forest cover, NFI report is generally higher than GFCC estimation; on the other side, remote sensing observed more forest cover than inventory for provinces with high forest cover. Reforestation reported by forestry inventory is observed by remote sensing data, however the net forest increase reported by NFI is not supported by GFCC due to the wide spread forest loss happened in China.
Keywords :
land cover; land use; terrain mapping; vegetation; vegetation mapping; AD 2000 to 2005; China; China National Forest Inventory; GFCC estimation; GLCF forest cover change map; Global Land Cover Facility; International Geosphere-Biosphere Programme; Landsat satellite image; NFI; forest definitions; global scale; inventory derived estimation; land cover mapping; land use mapping; minimum tree cover; moderate resolution remote sensing data; national scale; reforestation; regional scale; remote regions; remote sensing based forest area; remote sensing data; wide spread forest loss; Accuracy; Earth; Educational institutions; Forestry; Remote sensing; Satellites; Vegetation; China; Forest cover and change; forest inventory; remote sensing;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
DOI :
10.1109/IGARSS.2014.6947432